As AI develops at a rapid pace, design system documentation is struggling to keep up. Documentation now serves two very different audiences: humans and machines.
Since the rise of design systems, teams have relied on documentation to explain how components, tokens, and patterns should be used. Traditionally, this documentation was written by humans, for humans. It told designers and engineers which tokens to use, how much spacing was required, and which button variant belonged in a particular context.
But documentation was never designed to be read cover to cover, it was meant to be referenced. As a result of this, design system teams found clever ways to make documentation digestible. Pages were designed to be highly visual, scannable, and easy to navigate so users could quickly find an answer and continue with their work.
Now with the prevalence of AI tools and LLMs, the way we consume documentation is changing. Machines read documentation very differently from people, and documentation must evolve accordingly.
How people use documentation#
People rarely consume documentation in a linear way. When documentation is used in a non-linear way, it acts more like an encyclopedia rather than a novel.
Designers and engineers typically arrive with specific questions; they want to know which token to use, whether a component is appropriate for a situation, or how a pattern should behave. Once they find the answer, they leave. As a result of this behaviour, design system documentation has evolved around readability and comprehension. Good documentation is meant to reduce cognitive effort through visual hierarchy, examples, annotations, and approachable language.
However, even well-crafted documentation has limitations because it must serve people with different levels of expertise, technical knowledge, and accessibility needs. Documentation therefore becomes a balancing act between precision and usability. Maintaining this balance is time-consuming; documentation requires constant updates, refinement, and governance, yet it is often deprioritised when teams are under pressure or resources become scarce.
How AI uses documentation#
AI consumes documentation differently. Unlike humans, AI reads everything. It does not skim for visual cues or rely on intuition to fill in gaps. It processes the content directly, ignoring the visual presentation designed for human readers.
Humans can infer meaning from context; so when instructions are vague or incomplete, models compensate by generating assumptions or hallucinations. Documentation written for AI therefore benefits from a different writing style. Clarity matters, so repetition can be useful. Instructions should be direct, structured, and unambiguous. The ideal format for AI-readable documentation is not a polished documentation site but structured Markdown that can be easily parsed, indexed, and delivered through systems such as MCPs.
Man vs. machine#
The challenge is that these two audiences have conflicting needs.
Documentation written for humans depends on trust, readability, and flexibility. It often uses conversational language and leaves room for the reader’s discretion. This sort of documentation benefits from visual design decisions such as diagrams, examples, live previews, and annotations that improve comprehension.
Documentation for AI requires precision, consistency, and explicit instruction. It does not need visual hierarchy or carefully crafted prose. A simple Markdown file containing structured rules and guidance may be more effective than an elegant documentation site.
These differences can create tension for design system teams as the same documentation may no longer effectively serve both audiences.
What changes for design system teams#
This shift is no longer theoretical; if product and engineering teams are already using AI tools in their workflows, then design system documentation may already be falling behind. The challenge now is to create documentation that supports both human understanding and machine interpretation.
A layered approach will become necessary. Documentation for humans can continue to prioritise readability and visual hierarchy, while documentation for AI must focus on structured rules, accessibility requirements, and explicit guidance.
Although documentation has always been a pain point because of the effort required to maintain it, this work is becoming increasingly valuable. AI lowers the barrier to building products. More people, including those with little to no familiarity with a design system, will be able to generate interfaces and code quickly. As businesses push to ship products faster, teams will increasingly rely on AI-assisted workflows to make design and implementation decisions.
Well-structured documentation gives AI systems the context required to produce accessible, consistent, and on-brand experiences. At the same time, AI may also help design system teams manage documentation itself by assisting with updates, maintenance, and content generation.
Closing thoughts#
The traditional problem with design system documentation was getting people to read it. This problem has not disappeared with the rise of AI. A new problem has emerged: documentation must now work for both humans and machines.
The future of design system documentation is not just about readability, it is about interpretability. Design system teams that adapt early will create systems that are easier to scale, easier to adopt, and better prepared for AI-assisted product development.